Embodiments of the invention relate generally to a system and method for capturing images capable of use in super resolution image processing, and more particularly to a system and method for providing sub-pixel image shifts for capturing high resolution images for super resolution image processing. It incorporates rapid electro-optical elements to rotate the input polarization axis of light passing through one or more tilted birefringent optical elements, so as to provide for a plurality of image shift locations. The plurality of image shift locations provide for the generation of both super resolved images and a series of focused images for 3D reconstruction.
Super-resolution is a class of techniques that enhance the resolution of an imaging system. In some super-resolution techniques—termed optical super-resolution—the diffraction limit of systems is transcended, while in others—geometrical super-resolution—the resolution of digital imaging sensors is enhanced. The use of super-resolution techniques may be desirable in numerous applications, including, for example, for purposes of biometric identification, such as in systems that acquire contactless images of fingerprints and/or palm prints, as it is recognized that a threshold level of image resolution is required in the acquired images to provide Level IV biometric data performance levels—such as 1000 pixels-per-inch (PPI) or more.
In order to achieve such a level of high image resolution, a digital optical imaging system must have both high lens resolution and high pixel resolution. Often, the pixel resolution is the limiting factor due to cost and speed limitations. In some cases, high pixel resolution sensors are available, but are limited in speed and can be quite costly. In other cases, adequate pixel resolution is not possible using current digital image sensors.
A commonly employed super-resolution technique is the spatial frequency domain method described by Kim et al. (S. P. Kim, N. K. Bose, and H. M. Valenzuela. Recursive reconstruction of high resolution image from noisy under sampled multiframes. IEEE Transactions Acoustics, Speech, and Signal Processing, 20(6):1013-1027, June 1990.), where—through spatial frequency analysis of several images with sub-pixel image shifts—an improved image with greater resolution than the individual images can be generated. The sub-pixel image shifts utilized to provide the increased resolution are achieved either by shifting of the object or the image sensor.
It is recognized, however, that certain limitations are inherent with existing methods for achieving sub-pixel image shifts, such as the one described by Kim et al. For example, with respect to achieving sub-pixel image shifts by shifting the object, it is recognized that, in many cases, the object being imaged cannot be moved, or is stationary. Additionally, with respect to achieving sub-pixel image shifts by shifting the image sensor, such as by providing a small de-centering of the lenses or by the use of small optical wedges, it is recognized that cameras utilizing image sensor shifting are limited in speed due to the mechanical motion involved in shifting the sensor.
It would therefore be desirable to design a system and method of acquiring object images that solves the problem of inadequate pixel resolution of digital image sensors. It would further be desirable for such a system and method to provide faster, more repeatable, and more robust hardware for image shifting (i.e., sub-pixel image shifts) than is presently available for capturing images for super resolution image processing, without requiring motion of the object or sensor.